Value of clinical, functional, and oximetric data for the prediction of obstructive sleep apnea in obese patients

Objective: To evaluate the diagnostic value of clinical features, pulmonary function testing, blood gas tensions, and oximetric data for case finding of obstructive sleep apnea (OSA) before polysomnography (PSG) in a series o f consecutive overweight patients. Methods: We studied a population of 102 consecutive patients referred by an obesity clinic for suspected OSA, in whom body mass index was greater than or equal to 25 kg/m(2). The following tests were performed: clinical score (CS), pulmonary function tests (PFTs), measurement of arterial blood gas t ensions, nocturnal oximetry, and full-night PSG. Results: Six of 34 women and 34 of 68 men had OSA, defined by an apnea-hypo pnea index greater than or equal to 15. CS and the cumulative time spent be low 80% arterial oxygen saturation (SaO(2)) were higher, and PaO2, minimal SaO(2), and mean nocturnal Sao, (mSaO(2)) were lower in OSA patients than i n non-OSA patients. Logistic regression showed that sex, CS, and the ratio of FEV1 over forced expiratory volume in 0.5 s (an index of upper airway ob struction on flow-volume curves) and mSao(2), expressed as categorical vari ables, were independent predictors of OSA. None of these individual variabl es had a satisfactory diagnostic value for the diagnosis of OSA. A logistic regression model including sex and all continuous variables would have all owed us to predict the presence or absence of OSA confidently in 72.5% of t he population, in whom the positive predictive value of the model was 94% a nd the negative predictive value was 90%. Conclusion: In obese patients referred to a respiratory sleep laboratory an d evaluated by CS, PFTs, arterial blood gases, and oximetry, no individual sign or symptom may accurately predict the presence or absence of OSA. Prov ided that it is validated in prospective studies, a logistic regression mod el using these variables may be useful for the prediction of OSA.